Hardware-efficient deep convolutional neural networks

A method, comprising:

• receiving an input image;
• generating a frequency domain representation of the input image;
• in a convolutional neural network comprising a plurality of convolutional layers and at least one fully connected layer, extracting a plurality of features based at least in part on the frequency domain representation of the input image and a sparse, frequency domain representation of a convolutional weighting kernel, wherein the sparse, frequency domain representation of the convolutional weighting kernel comprises a dense matrix and one or more sparse matrices, wherein the extracting comprises, in a first convolutional layer of the plurality of convolutional layers, multiplying the frequency domain representation of the input image by the one or more sparse matrices and applying a nonlinear function to a result of the multiplying, and wherein prior to generation of a feature vector of the plurality of extracted features, an output of a last convolutional layer is multiplied by the dense matrix;
• classifying the input image based on the plurality of extracted features;
• and
• based on the classifying, identifying the input image as containing an object of interest.

One or more computer-readable storage media storing computer-executable instructions for recognizing images, the recognizing comprising:

• receiving an input image;
• generating a frequency domain representation of the input image;
• determining a sparse, frequency domain representation of a convolutional weighting kernel, the sparse, frequency domain representation comprising one or more sparse matrices and a dense matrix;
• in a plurality of convolutional layers of a deep convolutional neural network, processing the input image based on the frequency domain representation of the input image, the one or more sparse matrices, and a frequency domain nonlinear function;
• in a plurality of fully connected layers of the deep convolutional neural network, processing the input image based on an output of the plurality of convolutional layers;
• determining a plurality of extracted features based on an output of the plurality of fully connected layers, wherein prior to determination of a feature vector of the plurality of extracted features, an output of a last convolutional layer is multiplied by the dense matrix;
• classifying the input image based on the plurality of extracted features;
• and
• based on the classification, identifying the input image as containing an object of interest.